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1.
BMJ ; 378: e071249, 2022 07 20.
Article in English | MEDLINE | ID: covidwho-1950081

ABSTRACT

OBJECTIVE: To estimate waning of covid-19 vaccine effectiveness over six months after second dose. DESIGN: Cohort study, approved by NHS England. SETTING: Linked primary care, hospital, and covid-19 records within the OpenSAFELY-TPP database. PARTICIPANTS: Adults without previous SARS-CoV-2 infection were eligible, excluding care home residents and healthcare professionals. EXPOSURES: People who had received two doses of BNT162b2 or ChAdOx1 (administered during the national vaccine rollout) were compared with unvaccinated people during six consecutive comparison periods, each of four weeks. MAIN OUTCOME MEASURES: Adjusted hazard ratios for covid-19 related hospital admission, covid-19 related death, positive SARS-CoV-2 test, and non-covid-19 related death comparing vaccinated with unvaccinated people. Waning vaccine effectiveness was quantified as ratios of adjusted hazard ratios per four week period, separately for subgroups aged ≥65 years, 18-64 years and clinically vulnerable, 40-64 years, and 18-39 years. RESULTS: 1 951 866 and 3 219 349 eligible adults received two doses of BNT162b2 and ChAdOx1, respectively, and 2 422 980 remained unvaccinated. Waning of vaccine effectiveness was estimated to be similar across outcomes and vaccine brands. In the ≥65 years subgroup, ratios of adjusted hazard ratios for covid-19 related hospital admission, covid-19 related death, and positive SARS-CoV-2 test ranged from 1.19 (95% confidence interval 1.14 to 1.24)to 1.34 (1.09 to 1.64) per four weeks. Despite waning vaccine effectiveness, rates of covid-19 related hospital admission and death were substantially lower among vaccinated than unvaccinated adults up to 26 weeks after the second dose, with estimated vaccine effectiveness ≥80% for BNT162b2, and ≥75% for ChAdOx1. By weeks 23-26, rates of positive SARS-CoV-2 test in vaccinated people were similar to or higher than in unvaccinated people (adjusted hazard ratios up to 1.72 (1.11 to 2.68) for BNT162b2 and 1.86 (1.79 to 1.93) for ChAdOx1). CONCLUSIONS: The rate at which estimated vaccine effectiveness waned was consistent for covid-19 related hospital admission, covid-19 related death, and positive SARS-CoV-2 test and was similar across subgroups defined by age and clinical vulnerability. If sustained to outcomes of infection with the omicron variant and to booster vaccination, these findings will facilitate scheduling of booster vaccination.


Subject(s)
COVID-19 , SARS-CoV-2 , Adult , BNT162 Vaccine , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines , ChAdOx1 nCoV-19 , Cohort Studies , Electronic Health Records , Humans
2.
BMJ ; 378: e068946, 2022 07 20.
Article in English | MEDLINE | ID: covidwho-1950077

ABSTRACT

OBJECTIVE: To compare the effectiveness of the BNT162b2 mRNA (Pfizer-BioNTech) and the ChAdOx1 (Oxford-AstraZeneca) covid-19 vaccines against infection and covid-19 disease in health and social care workers. DESIGN: Cohort study, emulating a comparative effectiveness trial, on behalf of NHS England. SETTING: Linked primary care, hospital, and covid-19 surveillance records available within the OpenSAFELY-TPP research platform, covering a period when the SARS-CoV-2 Alpha variant was dominant. PARTICIPANTS: 317 341 health and social care workers vaccinated between 4 January and 28 February 2021, registered with a general practice using the TPP SystmOne clinical information system in England, and not clinically extremely vulnerable. INTERVENTIONS: Vaccination with either BNT162b2 or ChAdOx1 administered as part of the national covid-19 vaccine roll-out. MAIN OUTCOME MEASURES: Recorded SARS-CoV-2 positive test, or covid-19 related attendance at an accident and emergency (A&E) department or hospital admission occurring within 20 weeks of receipt of the first vaccine dose. RESULTS: Over the duration of 118 771 person-years of follow-up there were 6962 positive SARS-CoV-2 tests, 282 covid-19 related A&E attendances, and 166 covid-19 related hospital admissions. The cumulative incidence of each outcome was similar for both vaccines during the first 20 weeks after vaccination. The cumulative incidence of recorded SARS-CoV-2 infection 20 weeks after first-dose vaccination with BNT162b2 was 21.7 per 1000 people (95% confidence interval 20.9 to 22.4) and with ChAdOx1 was 23.7 (21.8 to 25.6), representing a difference of 2.04 per 1000 people (0.04 to 4.04). The difference in the cumulative incidence per 1000 people of covid-19 related A&E attendance at 20 weeks was 0.06 per 1000 people (95% CI -0.31 to 0.43). For covid-19 related hospital admission, this difference was 0.11 per 1000 people (-0.22 to 0.44). CONCLUSIONS: In this cohort of healthcare workers where we would not anticipate vaccine type to be related to health status, we found no substantial differences in the incidence of SARS-CoV-2 infection or covid-19 disease up to 20 weeks after vaccination. Incidence dropped sharply at 3-4 weeks after vaccination, and there were few covid-19 related hospital attendance and admission events after this period. This is in line with expected onset of vaccine induced immunity and suggests strong protection against Alpha variant covid-19 disease for both vaccines in this relatively young and healthy population of healthcare workers.


Subject(s)
COVID-19 , Viral Vaccines , BNT162 Vaccine , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines , Cohort Studies , Health Personnel , Humans , SARS-CoV-2 , Social Support
3.
BMC Med ; 20(1): 243, 2022 07 05.
Article in English | MEDLINE | ID: covidwho-1916958

ABSTRACT

BACKGROUND: While the vaccines against COVID-19 are highly effective, COVID-19 vaccine breakthrough is possible despite being fully vaccinated. With SARS-CoV-2 variants still circulating, describing the characteristics of individuals who have experienced COVID-19 vaccine breakthroughs could be hugely important in helping to determine who may be at greatest risk. METHODS: With the approval of NHS England, we conducted a retrospective cohort study using routine clinical data from the OpenSAFELY-TPP database of fully vaccinated individuals, linked to secondary care and death registry data and described the characteristics of those experiencing COVID-19 vaccine breakthroughs. RESULTS: As of 1st November 2021, a total of 15,501,550 individuals were identified as being fully vaccinated against COVID-19, with a median follow-up time of 149 days (IQR: ​107-179). From within this population, a total of 579,780 (<4%) individuals reported a positive SARS-CoV-2 test. For every 1000 years of patient follow-up time, the corresponding incidence rate (IR) was 98.06 (95% CI 97.93-98.19). There were 28,580 COVID-19-related hospital admissions, 1980 COVID-19-related critical care admissions and 6435 COVID-19-related deaths; corresponding IRs 4.77 (95% CI 4.74-4.80), 0.33 (95% CI 0.32-0.34) and 1.07 (95% CI 1.06-1.09), respectively. The highest rates of breakthrough COVID-19 were seen in those in care homes and in patients with chronic kidney disease, dialysis, transplant, haematological malignancy or who were immunocompromised. CONCLUSIONS: While the majority of COVID-19 vaccine breakthrough cases in England were mild, some differences in rates of breakthrough cases have been identified in several clinical groups. While it is important to note that these findings are simply descriptive and cannot be used to answer why certain groups have higher rates of COVID-19 breakthrough than others, the emergence of the Omicron variant of COVID-19 coupled with the number of positive SARS-CoV-2 tests still occurring is concerning and as numbers of fully vaccinated (and boosted) individuals increases and as follow-up time lengthens, so too will the number of COVID-19 breakthrough cases. Additional analyses, to assess vaccine waning and rates of breakthrough COVID-19 between different variants, aimed at identifying individuals at higher risk, are needed.


Subject(s)
COVID-19 Vaccines , COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Chickenpox Vaccine , Cohort Studies , England/epidemiology , Humans , Retrospective Studies , SARS-CoV-2 , Vaccination
4.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336821

ABSTRACT

Objective To describe the impact of the COVID-19 pandemic on safe prescribing, using the PINCER prescribing indicators;to implement complex prescribing indicators at national scale using GP data. Design Population based cohort study, with the approval of NHS England using the OpenSAFELY platform. Setting Electronic health record data from 56.8 million NHS patients’ general practice records. Participants All NHS patients registered at a GP practice using TPP or EMIS computer systems and recorded as at risk of at least one potentially hazardous PINCER indicator between September 2019 and September 2021. Main outcome measure Monthly trends and between-practice variation for compliance with 13 PINCER measures between September 2019 and September 2021. Results The indicators were successfully implemented across GP data in OpenSAFELY. Hazardous prescribing remained largely unchanged during the COVID-19 pandemic, with only small reductions in achievement of the PINCER indicators. There were transient delays in blood test monitoring for some medications, particularly ACE inhibitors. All indicators exhibited substantial recovery by September 2021. We identified 1,813,058 patients at risk of at least one hazardous prescribing event. Conclusion Good performance was maintained during the COVID-19 pandemic across a diverse range of widely evaluated measures of safe prescribing. Summary box WHAT IS ALREADY KNOWN ON THIS TOPIC Primary care services were substantially disrupted by the COVID-19 pandemic. Disruption to safe prescribing during the pandemic has not previously been evaluated. PINCER is a nationally adopted programme of activities that aims to identify and correct hazardous prescribing in GP practices, by conducting manual audit on subgroups of practices. WHAT THIS STUDY ADDS For the first time, we were able to successfully generate data on PINCER indicators for almost the whole population of England, in a single analysis. Our study is the most comprehensive assessment of medication safety during the COVID-19 pandemic in England, covering 95% of the population using well-validated measures. Good performance was maintained across many PINCER indicators throughout the pandemic. Delays in delivering some medication-related blood test monitoring were evident though considerable recovery was made by the end of the study period.

5.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329766

ABSTRACT

Background: From December 16th 2021, antivirals and neutralising monoclonal antibodies (nMABs) were available to treat high-risk non-hospitalised patients with COVID-19 in England. Aims To develop a framework for detailed near real-time monitoring of treatment deployment, to ascertain eligibility status for patients and to describe trends and variation in coverage of treatment between geographic, clinical and demographic groups. Methods With the approval of NHS England we conducted a retrospective cohort study using routine clinical data from 23.4m people in the OpenSAFELY-TPP database, approximately 40% of England's population. We implemented national eligibility criteria and generated descriptive statistics with detailed clinical, demographic and geographic breakdowns for patients receiving an antiviral or nMAB. Results We identified 50,730 non-hospitalised patients with COVID-19 between 11th December 2021 and 23rd February 2022 who were potentially eligible for antiviral and/or nMAB treatment. 6420 (15%) received treatment (sotrovimab 3600 (56%);molnupiravir 2680 (42%);nirmatrelvir/ritonavir (Paxlovid) 80 (1%);casirivimab 50 (1%);and remdesivir <5). The proportion treated varied by risk group, with the lowest proportion treated in those with liver disease (10%;95% CI 9-11). Treatment type also varied, with molnupiravir favoured over sotrovimab in only two high risk cohorts: Down syndrome (67%;95% CI 59-74) and HIV/AIDS (63%;95% CI 56-70). The proportion treated varied by ethnicity, from White (14%;95% CI 13-14) or Asian (13%;95% CI 12-14) to Black (9%;95% CI 8-11);by NHS Regions (from 6% (95% CI 5-6) in Yorkshire and the Humber to 17% (95% CI 16-18) in the East of England);and by rurality from 16% (95% CI 14-17) in "Rural - village and dispersed" to 10% (95% CI 10-11) in "Urban - conurbation". There was also lower coverage among care home residents (4%;95% CI 3-4), those with dementia (4%;95% CI 3-5), those with sickle cell disease (7%;95% CI 5-8), and in the most socioeconomically deprived areas (9%;95% CI 8-9, vs least deprived: 15%;95% CI 15-16). Patients who were housebound, or who had a severe mental illness had a slightly reduced chance of being treated (10%;95% CI 8-11 and 10%;95% CI 8-12, respectively). Unvaccinated patients were substantially less likely to receive treatment (5%;95% CI 4-6). Conclusions Using the OpenSAFELY platform we have developed and delivered a rapid, near real-time data-monitoring framework for the roll-out of antivirals and nMABs in England that can deliver detailed coverage reports in fine-grained clinical and demographic risk groups, using publicly auditable methods, using linked but pseudonymised patient-level NHS data in a highly secure Trusted Research Environment. Targeted activity may be needed to address apparent lower treatment coverage observed among certain groups, in particular (at present): different NHS regions, socioeconomically deprived areas, and care homes.

6.
Diagn Progn Res ; 6(1): 6, 2022 Feb 24.
Article in English | MEDLINE | ID: covidwho-1702772

ABSTRACT

BACKGROUND: Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection. METHODS: We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence. The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used: model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care. We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England. Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors. RESULTS: Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities: C-statistics 0.92-0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled. CONCLUSIONS: Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.

7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-312378

ABSTRACT

Since the first case was reported to the World Health Organisation in December 2019, SARS-CoV-2 (COVID-19) has caused social and economic devastation on a scale not seen since World War 2. As the milestone of 2 years of ‘living with the virus’ approaches, Governments and businesses are desperate to develop interventions that can facilitate the reopening of society whilst still protecting public health. As the roll-out of COVID-19 vaccinations has gathered pace worldwide, particularly in wealthier countries, those responsible for developing such interventions have begun to focus on the use of digital ‘COVID-19 Vaccine Passports’, which can be used to prove that an individual has had an approved COVID-19 vaccination (both doses where applicable). Governments hope that Vaccine Passports may be used to facilitate international travel and permit increased domestic liberties, for example allowing people to access public venues, to attend large gatherings, or to return to work without compromising personal safety and public health. “Yellow Fever certificates”, required to enter a specific list of countries maintained by the World Health Organisation, provide a precedent for this type of intervention. However, there are concerns that the use of COVID-19 Vaccine Passports could be viewed as a mechanism for introducing a mandatory vaccination policy, and there are also concerns that due to issues related to the unequal global distribution of effective vaccines and ‘the digital divide’ their use could exacerbate inequalities. Here we discuss the ethical and human rights implications of COVID-19 vaccine passports, based on a systematised literature review and documentary analysis. We find that in the context of a global public health emergency, COVID-19 vaccine passports (or, as we discuss, the broader status passes) are ethically and legally permissible under relevant human rights and international health regulations, provided they are designed, implemented, and used in accordance with the least infringement principle and the value of equality. We then set out 18 concrete recommendations for supranational bodies, national governments, and businesses to help ensure they develop and deploy COVID-19 Vaccine Passports accordingly.

8.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322685

ABSTRACT

On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required.   For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance.  This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform.

9.
JMIR Form Res ; 6(1): e31623, 2022 Jan 31.
Article in English | MEDLINE | ID: covidwho-1662516

ABSTRACT

BACKGROUND: Although advanced analytical techniques falling under the umbrella heading of artificial intelligence (AI) may improve health care, the use of AI in health raises safety and ethical concerns. There are currently no internationally recognized governance mechanisms (policies, ethical standards, evaluation, and regulation) for developing and using AI technologies in health care. A lack of international consensus creates technical and social barriers to the use of health AI while potentially hampering market competition. OBJECTIVE: The aim of this study is to review current health data and AI governance mechanisms being developed or used by Global Digital Health Partnership (GDHP) member countries that commissioned this research, identify commonalities and gaps in approaches, identify examples of best practices, and understand the rationale for policies. METHODS: Data were collected through a scoping review of academic literature and a thematic analysis of policy documents published by selected GDHP member countries. The findings from this data collection and the literature were used to inform semistructured interviews with key senior policy makers from GDHP member countries exploring their countries' experience of AI-driven technologies in health care and associated governance and inform a focus group with professionals working in international health and technology to discuss the themes and proposed policy recommendations. Policy recommendations were developed based on the aggregated research findings. RESULTS: As this is an empirical research paper, we primarily focused on reporting the results of the interviews and the focus group. Semistructured interviews (n=10) and a focus group (n=6) revealed 4 core areas for international collaborations: leadership and oversight, a whole systems approach covering the entire AI pipeline from data collection to model deployment and use, standards and regulatory processes, and engagement with stakeholders and the public. There was a broad range of maturity in health AI activity among the participants, with varying data infrastructure, application of standards across the AI life cycle, and strategic approaches to both development and deployment. A demand for further consistency at the international level and policies was identified to support a robust innovation pipeline. In total, 13 policy recommendations were developed to support GDHP member countries in overcoming core AI governance barriers and establishing common ground for international collaboration. CONCLUSIONS: AI-driven technology research and development for health care outpaces the creation of supporting AI governance globally. International collaboration and coordination on AI governance for health care is needed to ensure coherent solutions and allow countries to support and benefit from each other's work. International bodies and initiatives have a leading role to play in the international conversation, including the production of tools and sharing of practical approaches to the use of AI-driven technologies for health care.

10.
Br J Gen Pract ; 72(714): e63-e74, 2022 01.
Article in English | MEDLINE | ID: covidwho-1592598

ABSTRACT

BACKGROUND: The COVID-19 pandemic has disrupted healthcare activity. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM: To describe the volume and variation of coded clinical activity in general practice, taking respiratory disease and laboratory procedures as examples. DESIGN AND SETTING: Working on behalf of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD: Activity using Clinical Terms Version 3 codes and keyword searches from January 2019 to September 2020 are described. RESULTS: Activity recorded in general practice declined during the pandemic, but largely recovered by September. There was a large drop in coded activity for laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was the international normalised ratio test, with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 6.9). The pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as 'no change'. Respiratory infections exhibited a sustained drop, not returning to pre-pandemic levels by September. Asthma reviews experienced a small drop but recovered, whereas chronic obstructive pulmonary disease reviews remained below baseline. CONCLUSION: An open-source software framework was delivered to describe trends and variation in clinical activity across an unprecedented scale of primary care data. The COVD-19 pandemic led to a substantial change in healthcare activity. Most laboratory tests showed substantial reduction, largely recovering to near-normal levels by September, with some important tests less affected and recording of respiratory disease codes was mixed.


Subject(s)
COVID-19 , Cohort Studies , England/epidemiology , Humans , Pandemics , Primary Health Care , SARS-CoV-2 , State Medicine
11.
Br J Gen Pract ; 72(714): e51-e62, 2022 01.
Article in English | MEDLINE | ID: covidwho-1592597

ABSTRACT

BACKGROUND: On 8 December 2020 NHS England administered the first COVID-19 vaccination. AIM: To describe trends and variation in vaccine coverage in different clinical and demographic groups in the first 100 days of the vaccine rollout. DESIGN AND SETTING: With the approval of NHS England, a cohort study was conducted of 57.9 million patient records in general practice in England, in situ and within the infrastructure of the electronic health record software vendors EMIS and TPP using OpenSAFELY. METHOD: Vaccine coverage across various subgroups of Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts is described. RESULTS: A total of 20 852 692 patients (36.0%) received a vaccine between 8 December 2020 and 17 March 2021. Of patients aged ≥80 years not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2%, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Patients with pre-existing medical conditions were more likely to be vaccinated with two exceptions: severe mental illness (89.5%) and learning disability (91.4%). There were 275 205 vaccine recipients who were identified as care home residents (JCVI group 1; 91.2% coverage). By 17 March, 1 257 914 (6.0%) recipients had a second dose. CONCLUSION: The NHS rapidly delivered mass vaccination. In this study a data-monitoring framework was deployed using publicly auditable methods and a secure in situ processing model, using linked but pseudonymised patient-level NHS data for 57.9 million patients. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups.


Subject(s)
COVID-19 Vaccines , COVID-19 , Cohort Studies , Humans , Primary Health Care , SARS-CoV-2 , Vaccination
12.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-296145

ABSTRACT

National guidance was issued during the COVID-19 pandemic to switch patients on warfarin to direct oral anticoagulants (DOACs) where appropriate as these require less frequent blood testing. DOACs are not recommended for patients with mechanical heart valves. We conducted a retrospective cohort study of DOAC prescribing in people with a record of a mechanical heart valve between September 2019 and May 2021, and describe the characteristics of this population. We identified 15,457 individuals with a mechanical heart valve recorded in their records, of whom 1058 (6.8%) had been prescribed a DOAC during the study period. 767 individuals with a record of a mechanical heart valve were currently prescribed a DOAC as of May 31st 2021. This is suggestive of inappropriate prescribing of DOACs in individuals with mechanical heart valves. Direct alerts have been issued to clinicians through their EHR software informing the issue. We show that the OpenSAFELY platform can be used for rapid audit and feedback to mitigate the indirect health impacts of COVID-19 on the NHS. We will monitor changes in prescribing for this risk group over the following months.

13.
Br J Gen Pract ; 72(714): e63-e74, 2022 01.
Article in English | MEDLINE | ID: covidwho-1505838

ABSTRACT

BACKGROUND: The COVID-19 pandemic has disrupted healthcare activity. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM: To describe the volume and variation of coded clinical activity in general practice, taking respiratory disease and laboratory procedures as examples. DESIGN AND SETTING: Working on behalf of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD: Activity using Clinical Terms Version 3 codes and keyword searches from January 2019 to September 2020 are described. RESULTS: Activity recorded in general practice declined during the pandemic, but largely recovered by September. There was a large drop in coded activity for laboratory tests, with broad recovery to pre-pandemic levels by September. One exception was the international normalised ratio test, with a smaller reduction (median tests per 1000 patients in 2020: February 8.0; April 6.2; September 6.9). The pattern of recording for respiratory symptoms was less affected, following an expected seasonal pattern and classified as 'no change'. Respiratory infections exhibited a sustained drop, not returning to pre-pandemic levels by September. Asthma reviews experienced a small drop but recovered, whereas chronic obstructive pulmonary disease reviews remained below baseline. CONCLUSION: An open-source software framework was delivered to describe trends and variation in clinical activity across an unprecedented scale of primary care data. The COVD-19 pandemic led to a substantial change in healthcare activity. Most laboratory tests showed substantial reduction, largely recovering to near-normal levels by September, with some important tests less affected and recording of respiratory disease codes was mixed.


Subject(s)
COVID-19 , Cohort Studies , England/epidemiology , Humans , Pandemics , Primary Health Care , SARS-CoV-2 , State Medicine
14.
Br J Gen Pract ; 72(714): e51-e62, 2022 01.
Article in English | MEDLINE | ID: covidwho-1505837

ABSTRACT

BACKGROUND: On 8 December 2020 NHS England administered the first COVID-19 vaccination. AIM: To describe trends and variation in vaccine coverage in different clinical and demographic groups in the first 100 days of the vaccine rollout. DESIGN AND SETTING: With the approval of NHS England, a cohort study was conducted of 57.9 million patient records in general practice in England, in situ and within the infrastructure of the electronic health record software vendors EMIS and TPP using OpenSAFELY. METHOD: Vaccine coverage across various subgroups of Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts is described. RESULTS: A total of 20 852 692 patients (36.0%) received a vaccine between 8 December 2020 and 17 March 2021. Of patients aged ≥80 years not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2%, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Patients with pre-existing medical conditions were more likely to be vaccinated with two exceptions: severe mental illness (89.5%) and learning disability (91.4%). There were 275 205 vaccine recipients who were identified as care home residents (JCVI group 1; 91.2% coverage). By 17 March, 1 257 914 (6.0%) recipients had a second dose. CONCLUSION: The NHS rapidly delivered mass vaccination. In this study a data-monitoring framework was deployed using publicly auditable methods and a secure in situ processing model, using linked but pseudonymised patient-level NHS data for 57.9 million patients. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups.


Subject(s)
COVID-19 Vaccines , COVID-19 , Cohort Studies , Humans , Primary Health Care , SARS-CoV-2 , Vaccination
15.
J Am Med Inform Assoc ; 28(9): 2002-2008, 2021 08 13.
Article in English | MEDLINE | ID: covidwho-1393285

ABSTRACT

In this perspective we want to highlight the rise of what we call "digital phenotyping" or inferring insights about peopleãs health and behavior from their digital devices and data, and the challenges this introduces. Indeed, the collection, processing, and storage of data comes with significant ethical, security and data governance considerations. The COVID-19 pandemic has laid bare the importance of scientific data and modeling, both to understand the nature and spread of the disease, and to develop treatment. But digital devices have also played a (controversial) role, with track and trace systems and increasingly "vaccine passports" being rolled out to help societies open back up. These systems epitomize a wider and longer-standing trend towards seeing almost any form of personal data as potentially health data, especially with the rise of consumer health trackers and other gadgets. Here, we offer an overview of the risks this introduces, drawing on the earlier revolution in genomic sequencing, and propose guidelines to help protect privacy whilst utilizing personal data to help get society back up to speed.


Subject(s)
COVID-19 , Pandemics , Humans , Privacy , SARS-CoV-2
16.
Br J Gen Pract ; 71(712): e806-e814, 2021 11.
Article in English | MEDLINE | ID: covidwho-1339630

ABSTRACT

BACKGROUND: Long COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created. AIM: To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time. DESIGN AND SETTING: Population-based cohort study in English primary care. METHOD: Working on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week. RESULTS: Long COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4). CONCLUSION: Current recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians' understanding of the diagnosis.


Subject(s)
COVID-19 , Clinical Coding , COVID-19/complications , Cohort Studies , England , Female , Humans , Male , Primary Health Care
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